Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system for channeling elements in an analytics engine environment, the system comprising: a receiver including a decision engine; a plurality of channels; an analytics engine, wherein the plurality of channels connects the receiver to the analytics engine, wherein each channel of the plurality of channels is associated with a bandwidth, wherein the bandwidth is a transfer rate of the channel; a storage medium, the storage medium being coupled to a processor; the processor configured to: analyze a current process; identify critical data element types associated with the current process; receive a real-time data stream including a plurality of data elements; pre-filter the plurality of data elements, wherein the pre-filtering determines one or more of the plurality of data elements are associated with a critical data element type from the identified critical data element types; select a channel of a plurality of channels to fast-path the one or more of the plurality of data elements based at least in part on the pre-filtering, wherein the selected channel is based at least in part on the bandwidth and current availability each of the plurality of channels; increase a confidence level corresponding to the current process and the critical data element type, wherein the confidence level is increased based on a state of the current process; and provide feedback from the analytics engine directly to the receiver to dynamically update the critical element type during the current process to be pre-filtered based on the analyzing of the current process and increased confidence level, wherein the pre-filtering is based on metadata associated with each of the one or more of the plurality of data elements, wherein the receiver uses the updated critical data element type to pre-filter a different set of data elements.
This system optimizes data flow to an analytics engine. It includes a receiver with a decision engine, multiple channels connecting to an analytics engine (each with a defined transfer rate or bandwidth), storage, and a processor. The processor first analyzes a current operational process and identifies its crucial data types. It then receives a real-time data stream and pre-filters it using metadata to detect elements matching these critical types. For detected critical elements, the processor selects the most suitable channel (considering its transfer rate and current availability) to fast-path them to the analytics engine. The system tracks a confidence level for the process and critical data, increasing it based on the process's state. Crucially, the analytics engine provides direct feedback to the receiver, dynamically updating the critical data types used for pre-filtering based on ongoing process analysis and the updated confidence level, allowing the receiver to adapt filtering for new data.
2. The system of claim 1 , wherein the processor is further configured to store a record correlating the current process and critical data element type, and increasing the confidence level based on a performance of the current process.
This system, which optimizes data flow to an analytics engine, includes a receiver with a decision engine, multiple channels connecting to an analytics engine (each with a defined transfer rate), storage, and a processor. The processor analyzes a current operational process, identifies its crucial data types, receives and pre-filters real-time data using metadata to detect critical elements. For detected critical elements, the processor selects the most suitable channel (considering its transfer rate and current availability) to fast-path them to the analytics engine. The system tracks a confidence level for the process and critical data, increasing it based on the process's state. The analytics engine provides direct feedback to the receiver, dynamically updating critical data types for pre-filtering based on analysis and increased confidence. Furthermore, the processor stores a record correlating the current process and its critical data types, and enhances the confidence level adjustment by increasing it specifically based on the *performance* of the current process.
3. The system of claim 1 , wherein responsive to determining one or more of the plurality of data elements is associated with the critical data element type, tagging the pre-filtered one or more of the plurality of data elements to indicate the critical data element type for fast pathing over the selected channel of the plurality of channels; and transmitting the pre-filtered one or more of the plurality of data elements to the analytics engine over the selected channel based on the tag.
This system, which optimizes data flow to an analytics engine, includes a receiver with a decision engine, multiple channels connecting to an analytics engine (each with a defined transfer rate), storage, and a processor. The processor analyzes a current operational process, identifies its crucial data types, receives and pre-filters real-time data using metadata to detect critical elements. For detected critical elements, the processor selects the most suitable channel (considering its transfer rate and current availability) to fast-path them to the analytics engine. The system tracks a confidence level for the process and critical data, increasing it based on the process's state. The analytics engine provides direct feedback to the receiver, dynamically updating critical data types for pre-filtering based on analysis and increased confidence. Specifically, when the system determines a data element is critical, it *tags* that pre-filtered element to mark it for fast-pathing over the selected channel. This tagged critical data is then transmitted to the analytics engine over the previously selected fast-path channel, with the transmission being explicitly guided by the tag.
4. The system of claim 1 , wherein the processor is further configured to define a confidence level threshold corresponding to the current process and the critical element data type.
This system, which optimizes data flow to an analytics engine, includes a receiver with a decision engine, multiple channels connecting to an analytics engine (each with a defined transfer rate), storage, and a processor. The processor analyzes a current operational process, identifies its crucial data types, receives and pre-filters real-time data using metadata to detect critical elements. For detected critical elements, the processor selects the most suitable channel (considering its transfer rate and current availability) to fast-path them to the analytics engine. The system tracks a confidence level for the process and critical data, increasing it based on the process's state. The analytics engine provides direct feedback to the receiver, dynamically updating critical data types for pre-filtering based on analysis and increased confidence. Furthermore, the processor is configured to define a specific *confidence level threshold* that corresponds to the current process and its critical data element types, allowing for criteria to be set for the confidence level.
5. The system of claim 1 , wherein the processor is further configured to define a temporal threshold for the critical element data type based on the current process.
This system, which optimizes data flow to an analytics engine, includes a receiver with a decision engine, multiple channels connecting to an analytics engine (each with a defined transfer rate), storage, and a processor. The processor analyzes a current operational process, identifies its crucial data types, receives and pre-filters real-time data using metadata to detect critical elements. For detected critical elements, the processor selects the most suitable channel (considering its transfer rate and current availability) to fast-path them to the analytics engine. The system tracks a confidence level for the process and critical data, increasing it based on the process's state. The analytics engine provides direct feedback to the receiver, dynamically updating critical data types for pre-filtering based on analysis and increased confidence. Moreover, the processor is configured to define a *temporal threshold* (a time-based limit) for the critical data element type, which is determined based on the context of the current process.
6. A computer program product for channeling elements in an analytics engine environment, the computer program product comprising: a non-transitory computer readable storage medium having stored thereon first program instructions executable by a processor to cause the processor to: analyze a current process; identify critical data element types associated with the current process; receive a real-time data stream including a plurality of data elements; pre-filter the plurality of data elements, wherein the pre-filtering determines one or more of the plurality of data elements are associated with a critical data element type from the identified critical data element types; select a channel of a plurality of channels to fast-path the one or more of the plurality of data elements based at least in part on the pre-filtering, wherein the selected channel is based at least in part on a bandwidth and current availability each of the plurality of channels, wherein the plurality of channels connects a receiver to an analytics engine, wherein each channel of the plurality of channels is associated with a bandwidth, wherein the bandwidth is a transfer rate of the channel; increase a confidence level corresponding to the current process and the critical data element type, wherein the confidence level is increased based on a state of the current process; and provide feedback from the analytics engine directly to the receiver to dynamically update the critical element type during the current process to be pre-filtered based on the analyzing of the current process and increased confidence level, wherein the pre-filtering is based on metadata associated with each of the one or more of the plurality of data elements, wherein the receiver uses the updated critical data element type to pre-filter a different set of data elements.
This computer program product, stored on a non-transitory computer readable medium, contains instructions that, when executed by a processor, enable efficient data channeling within an analytics engine environment. The instructions cause the processor to analyze a current operational process and identify its crucial data types. It receives a real-time data stream and pre-filters it using metadata to detect elements matching these critical types. For detected critical elements, the processor selects the most suitable channel (considering its transfer rate and current availability) to fast-path them to an analytics engine, where these channels connect a receiver to the engine. The instructions also cause the system to track a confidence level for the process and critical data, increasing it based on the process's state. Furthermore, it causes feedback from the analytics engine to be provided directly to the receiver, dynamically updating the critical data types for future pre-filtering based on ongoing analysis and increased confidence, allowing the receiver to adapt filtering.
7. The computer program product of claim 6 , wherein the instructions are further executable by the processor to cause the processor to store a record correlating the current process and critical data element type, and increasing the confidence level based on a performance of the current process.
This computer program product, stored on a non-transitory computer readable medium, contains instructions that, when executed by a processor, enable efficient data channeling within an analytics engine environment. The instructions cause the processor to analyze a current operational process, identify its crucial data types, receive and pre-filter real-time data using metadata to detect critical elements. For detected critical elements, it selects the most suitable channel (considering its transfer rate and current availability) to fast-path them to an analytics engine, where these channels connect a receiver to the engine. The instructions also cause the system to track a confidence level for the process and critical data, increasing it based on the process's state. Feedback from the analytics engine dynamically updates critical data types for pre-filtering based on analysis and increased confidence. Additionally, the instructions further cause the processor to store a record correlating the current process and its critical data types, and to specifically increase the confidence level based on the *performance* of the current process.
8. The computer program product of claim 6 , wherein responsive to determining one or more of the plurality of data elements is associated with the critical data element type, tagging the pre-filtered one or more of the plurality of data elements to indicate the critical data element type for fast pathing over the selected channel of the plurality of channels, and transmitting the pre-filtered one or more plurality of data elements to the analytics engine over the selected channel based on the tag.
This computer program product, stored on a non-transitory computer readable medium, contains instructions that, when executed by a processor, enable efficient data channeling within an analytics engine environment. The instructions cause the processor to analyze a current operational process, identify its crucial data types, receive and pre-filter real-time data using metadata to detect critical elements. For detected critical elements, it selects the most suitable channel (considering its transfer rate and current availability) to fast-path them to an analytics engine, where these channels connect a receiver to the engine. The instructions also cause the system to track a confidence level for the process and critical data, increasing it based on the process's state. Feedback from the analytics engine dynamically updates critical data types for pre-filtering based on analysis and increased confidence. Specifically, upon determining a data element is critical, the instructions cause the processor to *tag* that pre-filtered element to mark it for fast-pathing over the selected channel. The instructions then further cause the processor to transmit this tagged critical data to the analytics engine over the selected channel, guided by the tag.
9. The computer program product of claim 6 , wherein the instructions are further executable by the processor to cause the processor to define at least one of a confidence level threshold corresponding to the current process and the critical element data type or a temporal threshold for the critical element data type based on the current process based on the current process.
This computer program product, stored on a non-transitory computer readable medium, contains instructions that, when executed by a processor, enable efficient data channeling within an analytics engine environment. The instructions cause the processor to analyze a current operational process, identify its crucial data types, receive and pre-filter real-time data using metadata to detect critical elements. For detected critical elements, it selects the most suitable channel (considering its transfer rate and current availability) to fast-path them to an analytics engine, where these channels connect a receiver to the engine. The instructions also cause the system to track a confidence level for the process and critical data, increasing it based on the process's state. Feedback from the analytics engine dynamically updates critical data types for pre-filtering based on analysis and increased confidence. Furthermore, the instructions additionally cause the processor to define either a *confidence level threshold* corresponding to the current process and critical data type, or a *temporal threshold* (a time-based limit) for the critical data type, both based on the current process.
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July 28, 2020
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